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Early-warning signals of topological collapse in interbank networks.

Squartini T, van Lelyveld I, Garlaschelli D - Sci Rep (2013)

Bottom Line: The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions.These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done.We discuss important implications for bank regulatory policies.

View Article: PubMed Central - PubMed

Affiliation: Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands.

ABSTRACT
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

No MeSH data available.


Related in: MedlinePlus

Temporal evolution of the dyadic z-scores:  under the DRG (top-left, purple circles) and the DCM (top-right, blue circles),  under the DRG (middle-left, purple, full squares) and the DCM (middle-right, blue, full squares),  under the DRG (bottom-left, purple, empty squares) and the DCM (bottom-right, blue, empty squares).
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f2: Temporal evolution of the dyadic z-scores: under the DRG (top-left, purple circles) and the DCM (top-right, blue circles), under the DRG (middle-left, purple, full squares) and the DCM (middle-right, blue, full squares),  under the DRG (bottom-left, purple, empty squares) and the DCM (bottom-right, blue, empty squares).

Mentions: However, we are going to show that the picture changes if, after controlling for the size and density themselves, we consider higher-order topological properties (dyadic, triadic, and so on). We first focus on the relative frequency or abundances of the three possible dyadic motifs in the observed network, i.e. the number L↔ of reciprocated (full) dyads, the number L↔ of non-reciprocated (single) dyads, and the number L↮ of empty dyads (see fig. 2). These numbers are informative only after filtering out size and density effects, or even more complicated topological properties. Therefore, here and in what follows, we compare each measured quantity X with the expected value 〈X〉 under a model which has some properties in common with the observed network but is otherwise maximally random. More precisely, we introduce z-scores (see Methods section) to quantify the deviation between data and model. Technically, the method we adopt is an analytical and unbiased one25 based on maximum-entropy ensembles of graphs with constraints26 (see SI for details). We stress that the use of a model is very different from that of a proper explanatory model: throughout the entire paper, we do not aim at introducing a model that accurately reproduces the data. Rather, the models we define represent different benchmarks, with various levels of complexity, that discount for the immediate effects of certain topological properties treated as constraints. Comparing the data with the predictions of a model allows us to determine which observed structural properties are not simply explained by the constraint specifying the model itself. Indeed, our most informative findings will correspond to a deviation, rather than an agreement, with models. It should therefore be clear that models are by construction in-sample, as it would make no sense to control, in one snapshot of the network, for the effects of a topological property observed in a different snapshot. The inherently in-sample nature of models is very different from the out-of-sample one of explanatory models, where the fit with one snapshot of the data is used to reproduce different snapshots.


Early-warning signals of topological collapse in interbank networks.

Squartini T, van Lelyveld I, Garlaschelli D - Sci Rep (2013)

Temporal evolution of the dyadic z-scores:  under the DRG (top-left, purple circles) and the DCM (top-right, blue circles),  under the DRG (middle-left, purple, full squares) and the DCM (middle-right, blue, full squares),  under the DRG (bottom-left, purple, empty squares) and the DCM (bottom-right, blue, empty squares).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3842548&req=5

f2: Temporal evolution of the dyadic z-scores: under the DRG (top-left, purple circles) and the DCM (top-right, blue circles), under the DRG (middle-left, purple, full squares) and the DCM (middle-right, blue, full squares),  under the DRG (bottom-left, purple, empty squares) and the DCM (bottom-right, blue, empty squares).
Mentions: However, we are going to show that the picture changes if, after controlling for the size and density themselves, we consider higher-order topological properties (dyadic, triadic, and so on). We first focus on the relative frequency or abundances of the three possible dyadic motifs in the observed network, i.e. the number L↔ of reciprocated (full) dyads, the number L↔ of non-reciprocated (single) dyads, and the number L↮ of empty dyads (see fig. 2). These numbers are informative only after filtering out size and density effects, or even more complicated topological properties. Therefore, here and in what follows, we compare each measured quantity X with the expected value 〈X〉 under a model which has some properties in common with the observed network but is otherwise maximally random. More precisely, we introduce z-scores (see Methods section) to quantify the deviation between data and model. Technically, the method we adopt is an analytical and unbiased one25 based on maximum-entropy ensembles of graphs with constraints26 (see SI for details). We stress that the use of a model is very different from that of a proper explanatory model: throughout the entire paper, we do not aim at introducing a model that accurately reproduces the data. Rather, the models we define represent different benchmarks, with various levels of complexity, that discount for the immediate effects of certain topological properties treated as constraints. Comparing the data with the predictions of a model allows us to determine which observed structural properties are not simply explained by the constraint specifying the model itself. Indeed, our most informative findings will correspond to a deviation, rather than an agreement, with models. It should therefore be clear that models are by construction in-sample, as it would make no sense to control, in one snapshot of the network, for the effects of a topological property observed in a different snapshot. The inherently in-sample nature of models is very different from the out-of-sample one of explanatory models, where the fit with one snapshot of the data is used to reproduce different snapshots.

Bottom Line: The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions.These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done.We discuss important implications for bank regulatory policies.

View Article: PubMed Central - PubMed

Affiliation: Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands.

ABSTRACT
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.

No MeSH data available.


Related in: MedlinePlus